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Cranioplasty is the procedure that is frequently used to treat deficiencies in the skull bone brought on by skull abnormalities in bone, traumatic skull fractures, bone malignancy, and infections. In surgery, biocompatible materials with unique properties have been utilized. Given the functionality and aesthetic standpoint, the human skull approach for repairing and restoring the abnormalities is hugely challenging. Polymethylmethacrylate (PMMA) cement, based on a dough-like material formed by combining two powdered polymer and liquid monomer components, is considered one of the earliest materials to treat cranial defects. Regarding the development of the latest additive manufacturing technologies, 3D printing has become a promising method for creating cranial implants customized to each patient. This study uses a solid basic medical grade PMMA material as a basis filament for the fused filament fabrication (FFF) process. This paper presents a high-quality model and manufacturing of a left parietal-temporal implant in the human skull based on computed tomography CT images. It evaluates the fitting accuracy and topographical roughness of the PMMA cranial implant. The results proved that the root means square error values for the deviation between the cranial implant's physical model and its planned model are (0.628, 0.833, and 0.650 mm) and implant roughness measurements are (2.5, 2.81, and 3.23 μm). The implemented framework outlined in the present study indicates that the contour continuity between implant-skull overlap, fitting accuracy, implant roughness topography, and the cosmetic outcome achieved entirely satisfactory results. Highlights: Reconstructed the skull defect by using mouldless 3D printing PMMA to manufacture high-quality patient-specific cranial implants directly. A qualitative and quantitative evaluation of implant dimensional accuracy is given and ensure the contour continuity between implant-skull overlap is ensured. Evaluate the cranial implant surface topography. [ABSTRACT FROM AUTHOR]

This study evaluates the performance of five satellite precipitation products (GPM IMERG, TRMM 3B42, ERA5, PERSIANN, and CHIRPS) compared to monthly observations from two weather stations (Laayoune and Essmara) over 2001–2017 using statistical metrics including correlation coefficient (CC) and mean square error (MSE). The results reveal notable differences between products. On a monthly timescale, GPM IMERG shows the best overall accuracy with a MSE of 16.8 mm/month. However, TRMM 3B42 exhibits higher temporal correlations with a CC around 0.83. The analysis provides insights into product capabilities and limitations for hydrological monitoring in data-sparse regions. Key findings include the superior performance of latest generation datasets like GPM alongside biases requiring localized calibration. The study delivers an assessment framework to guide integration of multiple satellite estimates for enhanced precipitation quantification and hydroclimatic modeling in water-stressed environments. [ABSTRACT FROM AUTHOR]

Several satellite gravity anomaly models are freely available to calculate the free‐air gravity anomaly in areas where shipborne gravity measurements are scarce. Two models produced by the Technical University of Denmark (DTU17) and the Scripps Institution of Oceanography (SIOv32.1), respectively, were selected to compute the free‐air anomalies over the Cosmonaut Sea, East Antarctica. A statistical comparison analysis was performed to evaluate the resolution of satellite gravity anomaly models by comparing them with the shipborne surveying date. The radially averaged energy spectra of free‐air anomaly from different sources were calculated and compared over two selected regions to further evaluate the reliability of the data derived from satellite gravity anomaly models. The satellite gravity anomaly models have a better resolution in the ocean basin than in the area near the continental shelf. The comparison analysis revealed that the precision of both DTU17 and SIOv32.1 is close to the shipborne gravity data, but on average, SIOv32.1 is a little bit better than DTU17. The spectral analysis showed that the shipborne measurements may provide higher resolution than the satellite gravity anomaly model at wavelengths shorter than 20 km, and the free‐air data derived from SIOv32.1 have better resolution than the one from DTU17. These shipborne datasets will provide contributions for the updates of the Antarctic gravity anomaly and enable new high‐resolution combined Earth gravity models to be derived in Antarctica. [ABSTRACT FROM AUTHOR]

Purpose: To verify a novel method that improves the accuracy of static computer-aided implant surgery (sCAIS) through intraoperative measurement. Materials and Methods: Forty-seven patients were selected for this study, each with a missing tooth or a tooth that required extraction from the anterior area. The patients were divided into the intraoperative measuring guide (MG) and conventional guide (CG) groups. Following the preoperative implant planning, the surgical guides were designed and fabricated. In the MG group, the drill was guided by double-armed zirconia sleeves, and the axial direction of the drill was assessed using the indicator components. The implant was guided using a resin guide tube. In the CG group, the drills were guided using a metal sleeve and handles, and the implants were placed with the guidance of the metal sleeve only. The angular and linear deviations at the entry and apex between the planned and actual implant positions were measured after matching the preoperative and postoperative CBCT data. The independent-samples t test was used to compare the deviation between the MG and CG groups. Results: The 3D deviations for the MG group at the entry and apex were 0.67 ± 0.44 mm and 0.93 ± 0.40 mm, respectively. The angular deviation was 2.27 ± 0.96 degrees. Statistical differences were found in the 3D deviation at the entry point and apical position between the MG and CG groups, yielding relatively smaller deviations in the MG group. Conclusion: The use of an intraoperative measuring guide could improve the accuracy of implant placement in sCAIS. [ABSTRACT FROM AUTHOR]

ObjectiveTo fully utilize and protect farmland and lay a solid foundation for the sustainable use of land, it is particularly important to obtain real-time and precise information regarding farmland area, distribution, and other factors. Leveraging remote sensing technology to obtain farmland data can meet the requirements of large-scale coverage and timeliness. However, the current research and application of deep learning methods in remote sensing for cultivated land identification still requires further improvement in terms of depth and accuracy. The objective of this study is to investigate the potential application of deep learning methods in remote sensing for identifying cultivated land in the hilly areas of Southwest China, to provide insights for enhancing agricultural land utilization and regulation, and for harmonizing the relationship between cultivated land and the economy and ecology.MethodsSantai county, Mianyang city, Sichuan province, China (30°42'34"~31°26'35"N, 104°43'04"~105°18'13"E) was selected as the study area. High-resolution imagery from two scenes captured by the Gaofen-6 (GF-6) satellite served as the primary image data source. Additionally, 30-meter resolution DEM data from the United States National Aeronautics and Space Administration (NASA) in 2020 was utilized. A land cover data product, SinoLC-1, was also incorporated for comparative evaluation of the accuracy of various extraction methods' results. Four deep learning models, namely Unet, PSPNet, DeeplabV3+, and Unet++, were utilized for remote sensing land identification research in cultivated areas. The study also involved analyzing the identification accuracy of cultivated land in high-resolution satellite images by combining the results of the random forest (RF) algorithm along with the deep learning models. A validation dataset was constructed by randomly generating 1 000 vector validation points within the research area. Concurrently, Google Earth satellite images with a resolution of 0.3 m were used for manual visual interpretation to determine the land cover type of the pixels where the validation points are located. The identification results of each model were compared using a confusion matrix to compute five accuracy evaluation metrics: Overall accuracy (OA), intersection over union (IoU), mean intersection over union (MIoU), F1-Score, and Kappa Coefficient to assess the cultivated land identification accuracy of different models and data products.Results and DiscussionsThe deep learning models displayed significant advances in accuracy evaluation metrics, surpassing the performance of traditional machine learning approaches like RF and the latest land cover product, SinoLC-1 Landcover. Among the models assessed, the UNet++ model performed the best, its F1-Score, IoU, MIoU, OA, and Kappa coefficient values were 0.92, 85.93%, 81.93%, 90.60%, and 0.80, respectively. DeeplabV3+, UNet, and PSPNet methods followed suit. These performance metrics underscored the superior accuracy of the UNet++ model in precisely identifying and segmenting cultivated land, with a remarkable increase in accuracy of nearly 20% than machine learning methods and 50% for land cover products. Four typical areas of town, water body, forest land and contiguous cultivated land were selected to visually compare the results of cultivated land identification results. It could be observed that the deep learning models generally exhibited consistent distribution patterns with the satellite imageries, accurately delineating the boundaries of cultivated land and demonstrating overall satisfactory performance. However, due to the complex features in remote sensing images, the deep learning models still encountered certain challenges of omission and misclassification in extracting cultivated land. Among them, the UNet++ model showed the closest overall extraction results to the ground truth and exhibited advantages in terms of completeness of cultivated land extraction, discrimination between cultivated land and other land classes, and boundary extraction compared to other models. Using the UNet++ model with the highest recognition accuracy, two types of images constructed with different features—solely spectral features and spectral combined with terrain features—were utilized for cultivated land extraction. Based on the three metrics of IoU, OA, and Kappa, the model incorporating both spectral and terrain features showed improvements of 0.98%, 1.10%, and 0.01% compared to the model using only spectral features. This indicated that fusing spectral and terrain features can achieve information complementarity, further enhancing the identification effectiveness of cultivated land.ConclusionsThis study focuses on the practicality and reliability of automatic cultivated land extraction using four different deep learning models, based on high-resolution satellite imagery from the GF-6 in Santai county in China. Based on the cultivated land extraction results in Santai county and the differences in network structures among the four deep learning models, it was found that the UNet++ model, based on UNet, can effectively improve the accuracy of cultivated land extraction by introducing the mechanism of skip connections. Overall, this study demonstrates the effectiveness and practical value of deep learning methods in obtaining accurate farmland information from high-resolution remote sensing imagery.

Understory terrain is an important fundamental indicator for monitoring changes in forest ecosystems. However, in-depth analysis of the accuracy of existing international open-source DEM products is still lacking for understory environments. The DEM data produced by Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) carries an advanced topographic LiDAR altimeter system (ATLAS) that contains elevation information of the ground surface as well as features such as the vegetation above. It can reflect the elevation of the ground surface, including the part obscured by vegetation or canopy, and can be better used to monitor changes in ground surface elevation. However, few existing studies have been analyzed the accuracy of open-source DEM products (include ATLAS, AW3D30 DEM, and TanDEM-X data) for estimating the ground in understory environments. Therefore, this study selected Aiken County, Cook Inlet, and Yucatan as study areas to evaluate ICESat-2/ATLAS data, AW3D30 DEM and TanDEM-X data using the airborne G-LiHT system's post-processing DTM. The study also quantitatively evaluated the influence of slope, aspect, and land vegetation type on elevation accuracies of different open-source DEM products. The experimental results showed that the ATLAS data at the footprint scale produced more accurate evaluation indicators than the AW3D30 DEM and TANDEM-X data products. The average evaluation indicator R2 was 0.98, RMSE was 1.44 m, and STD was 1.25 m. The results showed that ATLAS can be used as validation data to evaluate the accuracy of opensource DEM in estimating the vertical direction. At the regional scale, AW3D30 was the most robust and had the most stable performance in most of the tests (R2 is 0.95, RMSE is 6.37 m and STD is 5.02 m) and is therefore the best choice for research on understory terrain. Slope had a greater impact on the accuracy of data estimation compared to aspect and surface vegetation type. The difference in RMSE for estimating forest understory terrain was 3.11 m for ATLAS products, 29.37 m for AW3D30 DEM data and 54.26 m for TanDEM-X data over the different slopes. [ABSTRACT FROM AUTHOR]

[Objective] To fully utilize and protect farmland and lay a solid foundation for the sustainable use of land, it is particularly important to obtain real-time and precise information regarding farmland area, distribution, and other factors. Leveraging remote sensing technology to obtain farmland data can meet the requirements of large-scale coverage and timeliness. However, the current research and application of deep learning methods in remote sensing for cultivated land identification still requires further improvement in terms of depth and accuracy. The objective of this study is to investigate the potential application of deep learning methods in remote sensing for identifying cultivated land in the hilly areas of Southwest China, to provide insights for enhancing agricultural land utilization and regulation, and for harmonizing the relationship between cultivated land and the economy and ecology.[Methods] Santai county, Mianyang city, Sichuan province, China（30°42’34 "～31°26’35" N, 104°43’04 "～105°18’13" E） was selected as the study area. High-resolution imagery from two scenes captured by the Gaofen-6（GF-6） satellite served as the primary image data source. Additionally, 30-meter resolution DEM data from the United States National Aeronautics and Space Administration（NASA） in 2020 was utilized. A land cover data product, SinoLC-1, was also incorporated for comparative evaluation of the accuracy of various extraction methods’ results. Four deep learning models, namely Unet, PSPNet, DeeplabV3+, and Unet++, were utilized for remote sensing land identification research in cultivated areas. The study also involved analyzing the identification accuracy of cultivated land in high-resolution satellite images by combining the results of the random forest（RF） algorithm along with the deep learning models. A validation dataset was constructed by randomly generating 1 000 vector validation points within the research area. Concurrently, Google Earth satellite images with a resolution of 0.3 m were used for manual visual interpretation to determine the land cover type of the pixels where the validation points are located. The identification results of each model were compared using a confusion matrix to compute five accuracy evaluation metrics: Overall accuracy（OA）, intersection over union（IoU）, mean intersection over union（MIoU）, F1-Score, and Kappa Coefficient to assess the cultivated land identification accuracy of different models and data products.[Results and Discussions] The deep learning models displayed significant advances in accuracy evaluation metrics, surpassing the performance of traditional machine learning approaches like RF and the latest land cover product, SinoLC-1 Landcover. Among the models assessed, the UNet++ model performed the best, its F1-Score, IoU, MIoU, OA, and Kappa coefficient values were 0.92,85.93%, 81.93%, 90.60%, and 0.80, respectively. DeeplabV3+, UNet, and PSPNet methods followed suit. These performance metrics underscored the superior accuracy of the UNet++ model in precisely identifying and segmenting cultivated land, with a remarkable increase in accuracy of nearly 20% than machine learning methods and 50% for land cover products. Four typical areas of town, water body, forest land and contiguous cultivated land were selected to visually compare the results of cultivated land identification results.It could be observed that the deep learning models generally exhibited consistent distribution patterns with the satellite imageries, accurately delineating the boundaries of cultivated land and demonstrating overall satisfactory performance. However, due to the complex features in remote sensing images, the deep learning models still encountered certain challenges of omission and misclassification in extracting cultivated land. Among them, the UNet++ model showed the closest overall extraction results to the ground truth and exhibited advantages in terms of completeness of cultivated land extraction, discrimination between cultivated land and other land classes, and boundary extraction compared to other models. Using the UNet++ model with the highest recognition accuracy, two types of images constructed with different features—solely spectral features and spectral combined with terrain features—were utilized for cultivated land extraction. Based on the three metrics of IoU, OA, and Kappa, the model incorporating both spectral and terrain features showed improvements of 0.98%, 1.10%, and 0.01% compared to the model using only spectral features. This indicated that fusing spectral and terrain features can achieve information complementarity, further enhancing the identification effectiveness of cultivated land.[Conclusions] This study focuses on the practicality and reliability of automatic cultivated land extraction using four different deep learning models, based on high-resolution satellite imagery from the GF-6 in Santai county in China. Based on the cultivated land extraction results in Santai county and the differences in network structures among the four deep learning models, it was found that the UNet++ model, based on UNet, can effectively improve the accuracy of cultivated land extraction by introducing the mechanism of skip connections. Overall, this study demonstrates the effectiveness and practical value of deep learning methods in obtaining accurate farmland information from high-resolution remote sensing imagery.  [ABSTRACT FROM AUTHOR]

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Ground experimentation is an important method to verify the performance and quality of spacecraft structures. How to simulate the force and heat fields that a spacecraft may be subjected to during flight is a challenge for experiment design. In this paper, the static strength experimental method of spacecraft in the combined force and heat fields is studied. Furthermore, the coupling cases of the spacecraft during the flight process are simulated using an airbag and a resistive heater. Finally, it can be concluded that the accuracy of the proposed experimental method as well as the structural strength of the spacecraft can meet technical requirements during flight. [ABSTRACT FROM AUTHOR]

Machine learning (ML)–based clinical decision support tools (CDSTs) make personalized predictions for different treatments; by comparing predictions of multiple treatments, these tools can be used to optimize decision making for a particular patient. However, CDST prediction accuracy varies for different patients and also for different treatment options. If these differences are sufficiently large and consistent for a particular subcohort of patients, then that bias may result in those patients not receiving a particular treatment. Such level of bias would deem the CDST "unfair." The purpose of this study is to evaluate the "fairness" of ML CDST-based clinical outcomes predictions after anatomic (aTSA) and reverse total shoulder arthroplasty (rTSA) for patients of different demographic attributes. Clinical data from 8280 shoulder arthroplasty patients with 19,249 postoperative visits was used to evaluate the prediction fairness and accuracy associated with the following patient demographic attributes: ethnicity, sex, and age at the time of surgery. Performance of clinical outcome and range of motion regression predictions were quantified by the mean absolute error (MAE) and performance of minimal clinically important difference (MCID) and substantial clinical benefit classification predictions were quantified by accuracy, sensitivity, and the F1 score. Fairness of classification predictions leveraged the "four-fifths" legal guideline from the US Equal Employment Opportunity Commission and fairness of regression predictions leveraged established MCID thresholds associated with each outcome measure. For both aTSA and rTSA clinical outcome predictions, only minor differences in MAE were observed between patients of different ethnicity, sex, and age. Evaluation of prediction fairness demonstrated that 0 of 486 MCID (0%) and only 3 of 486 substantial clinical benefit (0.6%) classification predictions were outside the 20% fairness boundary and only 14 of 972 (1.4%) regression predictions were outside of the MCID fairness boundary. Hispanic and Black patients were more likely to have ML predictions out of fairness tolerance for aTSA and rTSA. Additionally, patients <60 years old were more likely to have ML predictions out of fairness tolerance for rTSA. No disparate predictions were identified for sex and no disparate regression predictions were observed for forward elevation, internal rotation score, American Shoulder and Elbow Surgeons Standardized Shoulder Assessment Form score, or global shoulder function. The ML algorithms analyzed in this study accurately predict clinical outcomes after aTSA and rTSA for patients of different ethnicity, sex, and age, where only 1.4% of regression predictions and only 0.3% of classification predictions were out of fairness tolerance using the proposed fairness evaluation method and acceptance criteria. Future work is required to externally validate these ML algorithms to ensure they are equally accurate for all legally protected patient groups. [ABSTRACT FROM AUTHOR]

Weighted total least squares (WTLS) have been regarded as the standard tool for the errors-in-variables (EIV) model in which all the elements in the observation vector and the coefficient matrix are contaminated with random errors. However, in many geodetic applications, some elements are error-free and some random observations appear repeatedly in different positions in the augmented coefficient matrix. It is called the linear structured EIV (LSEIV) model. Two kinds of methods are proposed for the LSEIV model from functional and stochastic modifications. On the one hand, the functional part of the LSEIV model is modified into the errors-in-observations (EIO) model. On the other hand, the stochastic model is modified by applying the Moore-Penrose inverse of the cofactor matrix. The algorithms are derived through the Lagrange multipliers method and linear approximation. The estimation principles and iterative formula of the parameters are proven to be consistent. The first-order approximate variance-covariance matrix (VCM) of the parameters is also derived. A numerical example is given to compare the performances of our proposed three algorithms with the STLS approach. Afterwards, the least squares (LS), total least squares (TLS) and linear structured weighted total least squares (LSWTLS) solutions are compared and the accuracy evaluation formula is proven to be feasible and effective. Finally, the LSWTLS is applied to the field of deformation analysis, which yields a better result than the traditional LS and TLS estimations.

Numerous spatiotemporal fusion (STF) methods have been developed to generate surface reflectance data with high spatial and temporal resolutions for dynamic monitoring. Although comparative studies have been conducted to assess various fusion methods, selecting the most suitable fusion method for acquiring long-term time series data remains a challenge. This article compared four representative STF methods based on the effect of 8 × n-day (n = 1, 2, …, 7) time scales between base and predicted data. These methods included the spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), enhanced STARFM (ESTARFM), and sensor-bias driven spatio-temporal fusion model (BiaSTF). Accuracy was assessed using metrics such as the root-mean-square error, correlation coefficients, erreur relative globale adimensionnelle de synthèse, and spectral angle mapper. The results indicate that as the time scale increases, fusion accuracy decreases, with a significant drop observed at the 40-day mark. Compared with the 8-day scale, at the 40-day scale, the ERGAS of STARFM decreased by 20.66%, that of FSDAF by 17.00%, that of ESTARFM by 14.37%, and that of BiaSTF by 11.48%. Furthermore, STF methods based on two pairs of images demonstrate a notable advantage in capturing data from distant temporal phases. In regions with pronounced phenological changes and longer time scales, BiaSTF consistently exhibits the best fusion performance (ERGAS = 1.67), followed by ESTARFM (ERGAS = 1.85). These findings can aid in determining the most suitable STF methods and provide guidelines for the development of new methods.

Accurate and reliable mangrove datasets are essential for the protection and management of mangrove ecosystems. Therefore, the evaluation of the current mangrove datasets and understanding the differences among them are critical. This study takes the Guangxi Beibu Gulf (GBG) and Guangdong-Hong Kong-Macao Greater Bay Area (GBA) as the study areas and analyzes the agreement and accuracy of eight mangrove datasets from 2000 to 2020 using area comparison, spatial agreement analysis, and absolute accuracy evaluation. The results show that; 1) significant differences exist in mangrove area and spatial distribution among the different mangrove datasets, with the percentage of high agreement areas ranging from 10% to 42%. 2) The overall accuracy of the evaluated mangrove datasets ranges from 56.2% to 95.6%, and the classification accuracy of mangrove datasets in inland areas is lower than the overall level. 3) There are regional differences in the quality of mangrove datasets, with the agreement and accuracy of mangrove datasets in the GBG being greater than those in the GBA. 4) Fine-scale mangrove mapping based on high-resolution remote sensing images, such as Sentinel, and global mangrove mapping based on the Google Earth Engine (GEE) cloud platform should be emphasized in the future. The findings of this study can provide guidance for data users to select appropriate mangrove datasets and a reference for future mangrove mapping research.

Various land use and land cover (LULC) products have been produced over the past decade with the development of remote sensing technology. Despite the differences in LULC classification schemes, there is a lack of research on assessing the accuracy of their application to croplands in a unified framework. Thus, this study evaluated the spatial and area accuracies of cropland classification for four commonly used global LULC products (i.e., MCD12Q1 V6, GlobCover2009, FROM-GLC and GlobeLand30) based on the harmonised FAO criterion, and quantified the relationships between four factors (i.e., slope, elevation, field size and crop system) and cropland classification agreement. The validation results indicated that MCD12Q1 and GlobeLand30 performed well in cropland classification regarding spatial consistency, with overall accuracies of 94.90 and 93.52%, respectively. The FROM-GLC showed the worst performance, with an overall accuracy of 83.17%. Overlaying the cropland generated by the four global LULC products, we found the proportions of complete agreement and disagreement were 15.51 and 44.72% for the cropland classification, respectively. High consistency was mainly observed in the Northeast China Plain, the Huang-Huai-Hai Plain and the northern part of the Middle-lower Yangtze Plain, China. In contrast, low consistency was detected primarily on the eastern edge of the northern and semiarid region, the Yunnan-Guizhou Plateau and southern China. Field size was the most important factor for mapping cropland. For area accuracy, compared with China Statistical Yearbook data at the provincial scale, the accuracies of different products in descending order were: GlobeLand30, FROM-GLC, MCD12Q1, and GlobCover2009. The cropland classification schemes mainly caused large area deviations among the four products, and they also resulted in the different ranks of spatial accuracy and area accuracy among the four products. Our results can provide valuable suggestions for selecting cropland products at the national or provincial scale and help cropland mapping and reconstruction, which is essential for food security and crop management, so they can also contribute to achieving the Sustainable Development Goals issued by the United Nations.

ABSTRACTLand cover is an important variable for climate, hydrology, and ecology studies. With the availability of various high-resolution global land cover (GLC) products, conducting a comprehensive assessment on their accuracy and consistency is important. In this study, we compared the performance of three latest 10-m-resolution GLC products, which include FROMGLC10 in 2017, ESA’s Worldcover10 in 2020, and ESRIGLC10 in 2020, and three latest 30-m-resolution GLC products, which include FROMGLC30 in 2017, GLC_FCS30 in 2020, and Globeland30 in 2020, in China. The consistency of these products was investigated in terms of spatial consistency and area consistency. Though the six GLC products demonstrate similar overall distribution patterns, their detailed spatial distributions are quite different, especially for the three 10-m-resolution products. Evidently, the cropland, forest, grassland, and bareland exhibited high inconsistencies than the other types. The classification accuracy of the six GLC products was also quantitatively assessed based on a visual-interpretation-based reference dataset. FROMGLC10 exhibits the highest overall accuracy of 65.57%, followed by FROMGLC30 (64.96%) and Worldcover10 (62.74%). ESRIGLC10 (49.79%) exhibits the lowest accuracy. The accuracies of shrubland, wetland, and tundra were relatively low. This study provides a valuable reference for selecting appropriate GLC products for potential users.

A high-quality dataset is a basic requirement to ensure the training quality and prediction accuracy of a deep learning network model (DLNM). To explore the influence of label image accuracy on the performance of a concrete crack segmentation network model in a semantic segmentation dataset, this study uses three labelling strategies, namely pixel-level fine labelling, outer contour widening labelling and topological structure widening labelling, respectively, to generate crack label images and construct three sets of crack semantic segmentation datasets with different accuracy. Four semantic segmentation network models (SSNMs), U-Net, High-Resolution Net (HRNet)V2, Pyramid Scene Parsing Network (PSPNet) and DeepLabV3+, were used for learning and training. The results show that the datasets constructed from the crack label images with pix-el-level fine labelling are more conducive to improving the accuracy of the network model for crack image segmentation. The U-Net had the best performance among the four SSNMs. The Mean Intersection over Union (MIoU), Mean Pixel Accuracy (MPA) and Accuracy reached 85.47%, 90.86% and 98.66%, respectively. The average difference between the quantized width of the crack image segmentation obtained by U-Net and the real crack width was 0.734 pixels, the maximum difference was 1.997 pixels, and the minimum difference was 0.141 pixels. Therefore, to improve the segmentation accuracy of crack images, the pixel-level fine labelling strategy and U-Net are the best choices. [ABSTRACT FROM AUTHOR]

Temperature and precipitation are crucial indicators for investigating climate changes, necessitating precise measurements for rigorous scientific inquiry. While the Fifth Generation of European Centre for Medium-Range Weather Forecasts Atmospheric Reanalysis (ERA5), ERA5 of the Land Surface (ERA5-Land), and China Meteorological Forcing Dataset (CMFD) temperature and precipitation products are widely used worldwide, their suitability for the Altay region of arid and semi-arid areas has received limited attention. Here, we used the Altay region as the study area, utilizing meteorological station data and implementing the residual revision method for temperature and the coefficient revision method for precipitation to rectify inaccuracies in monthly temperature and precipitation records from ERA5-Land, ERA5, and CMFD. We evaluate the accuracy of these datasets before and after correction using bias, Taylor diagrams, and root-mean-square error (RMSE) metrics. Additionally, we employ Tropical Rainfall Measuring Mission satellite precipitation data (TRMM) as a benchmark to assess the performance of ERA5-Land, ERA5, and CMFD monthly precipitation before and after correction. The results revealed significant differences in the temperature and precipitation capture capabilities of ERA5-Land, ERA5, and CMFD in the Altay region. Overall, these data exhibit substantial errors and are not directly suitable for scientific research. However, we applied residual and coefficient revision methods. After this revision, ERA5-Land, ERA5, and CMFD showed significantly improved temperature and precipitation capture capabilities, especially for ERA5-Land. In terms of temperature, post-revision-CMFD (CMFDPR) demonstrated better temperature capture capabilities. All three datasets showed weaker performance in mountainous regions compared to plains. Notably, post-revision-ERA5 (ERA5PR) seemed unsuitable for capturing temperature in the Altay region. Concerning rain, CMFDPR, post-revision-ERA5-Land (ERA5-LandPR) and ERA5PR outperformed TRMM in capturing precipitation. CMFDPR and ERA5-LandPR both outperform ERA5PR. In summary, the revision datasets effectively compensated for the sparse distribution of meteorological stations in the Altay region, providing reliable data support for studying climate change in arid and semi-arid areas. [ABSTRACT FROM AUTHOR]

Satellite-derived bathymetry (SDB) with high spatial resolution effectively maps detailed information about shallow sea depths. Properly selecting atmospheric correction (AC) methods is crucial to obtaining precise bathymetric information from satellite data. In this work, three different AC methods (FLAASH, 6S, and DOS) were applied to GaoFen-2 imagery in Lingyang Reef, South China Sea. Water depth retrieval models, such as single-band, multiband, and band ratio models, were established by 470 points of in-situ water depth data and used for evaluating the model performances. Additionally, the optimal model was applied to depth inversion. The results show that the multiband model based on four bands performs well in this study area with R2=0.736. The choice of AC methods significantly affects SDB, although acceptable results can be derived without these methods. Specifically, the DOS method has the highest inversion accuracy, with a mean relative error and root mean square error of 14.05% and 3.31 m, respectively. Furthermore, the inversion accuracy in various depth ranges may be primarily influenced by suspended sediment concentration and bottom-type uniformity. This study provides a valuable reference for selecting AC approaches and inversion models in high-spatial-resolution SDB in shallow seas. [ABSTRACT FROM AUTHOR]

As the key route detection device, the performance of marine LiDAR in harsh environments is of great importance. In this paper, a metric reliability analysis method for marine LiDAR systems under extreme wind loads is proposed. First, a static measurement accuracy evaluation model for the LiDAR system is proposed, targeting the problem that the LiDAR measurement tail reduces the measurement accuracy. Second, the distribution of extreme wind speeds in the Pacific Northwest is investigated, and a wind load probability model is developed. Finally, the impact of hull fluctuations on LiDAR measurement accuracy is analyzed by performing hull fluctuation simulations based on the wind load probability model, and the relationship curve between the metric reliability and measurement accuracy of marine LiDAR systems under extreme wind loads is addressed using the Monte-Carlo method. Experimental results show that the proposed LiDAR static measurement accuracy evaluation model can improve the measurement accuracy by more than 30%. Meanwhile, the solved curve of the LiDAR metric reliability versus the measurement allowable error indicates that the metric reliability can reach above 0.89 when the allowable error is 60 mm, which is instructive for the reliable measurement of marine LiDAR systems during ship navigation. [ABSTRACT FROM AUTHOR]

Cube-machining test is a possible method for evaluating the accuracy of five-axis machining centers. Although the geometric errors of rotary axes can be evaluated by measuring the height and inclination errors of the machined surfaces, other error sources, such as tool length and profile errors, influence the machining accuracy. Clarifying the factors that influence the machined result is necessary to correctly evaluate the machine tool accuracy based on the cube-machining test results. In this study, the effect of tool path and Z-axis reversal error motion on the cube-machining test accuracy was investigated. Actual machining tests were conducted with three types of the reversal error conditions of Z-axis. The Z-axis reversal error directly affected the results owing to different Z-axis motions for each surface of the workpiece. Based on the investigation, for the machining, a tool path pattern, which can reduce the influence of the Z-axis reversal errors by restricting the motion direction changes of Z-axis for all surfaces, was proposed. According to the Z-axis reversal error test results, the surfaces machined using the proposed tool path were not affected by the reversal error.

Study Region: Four subbasins in the Gandaki River Basin in the southern Himalayan region. Study Focus: In the southern Himalayan region, gridded precipitation datasets are often used to complement sparse gauge network observations in hydrological modelling for water resource assessment. However, the applicability and uncertainty of using these datasets in hydrological modelling remain to be evaluated. In this study, nine high spatial resolution gridded precipitation datasets were used to force a widely used hydrological model to simulate the daily runoff at four hydrological stations in this region. The accuracy of these datasets was evaluated, and the model calibration results and modelling uncertainty were analyzed. New Hydrological Insights for the Region: Our analysis revealed large differences among the nine gridded precipitation datasets, and most datasets underestimated the annual precipitation in the low-elevation regions and overestimated that in the high Himalayas. Furthermore, through the adjustments of model parameters, the underestimations of precipitation by gridded datasets were compensated for by the overestimation of glacial melt. Therefore, the model performance was better, and the model uncertainty was lower in basins with higher glacial coverage. Notably, our findings indicated that the performances of gridded precipitation datasets were heterogeneous and that their direct use in hydrological modelling may result in unreasonable hydrological process variables; thus, these datasets should be evaluated and used with caution in water resource assessments in Himalayan regions.

Forest canopy height data are essential to assessing forest biomass and carbon storage, and they can be efficiently retrieved using the Advanced Terrain Laser Altimetry System (ATLAS) onboard ICESat-2. However, due to the sparse and discrete distribution of ATLAS footprints, the wall-to-wall mapping of forest canopy heights requires a combination of other ancillary data. In order to match the ATLAS data with ancillary data, or estimate higher-resolution canopy heights, the standard ATLAS canopy height products (ATL08), reported at a fixed step size of 100 m (12 m × 100 m), are typically divided into 30 m step sizes. There is little concern about the accuracy of the generated 30 m segment (12 m × 30 m) dataset. Furthermore, previous studies have primarily evaluated the along-track performance of the canopy height product, rather than the ability of ATLAS to provide an accurate representation of canopy heights at the pixel-wise level. In this study, we use airborne LiDAR data as references with which to evaluate the along-track accuracy of 30 m segment canopy height products and their accuracy in representing canopy height at the pixel-wise level. A framework method for spatially matching ATLAS canopy height estimate data with ancillary data accurately is also established in this study. This enables the use of ATLAS data to characterize pixel-wise canopy heights more precisely. The results show that the accuracy of the 30 m segment version dataset in representing the pixel-wise canopy heights (R2 = 0.38, RMSE = 8.37 m) is lower than its along-track accuracy (R2 = 0.44, RMSE = 7.63 m). Using only the nighttime data for the Taranaki region, the proposed method improves the estimation of pixel-wise forest canopy heights, with the R2 increasing from 0.49 to 0.59, the RMSE decreasing from 7.48 m to 5.51 m, and the %RMSE decreasing from 36.7% to 27.6%. This study contributes to understanding the accuracy of the ATLAS in reflecting pixel-wise canopy height and provides a new way of spatially matching ATLAS canopy height data with other remote sensing data. [ABSTRACT FROM AUTHOR]

The ultimate goals of Augmented Reality (AR) applications are to provide a better management and ubiquitous access to information using seamless techniques in which the interactive real world is combined with an interactive computer-generated world, creating one coherent environment. The performance of the AR algorithm in terms of brightness, rotation, and scale in the real environment had been addressed as an issue due to the controlled environment. Hence, a performance evaluation of the AR algorithm in a real controlled environment is proposed in this paper. BRISK detector, FREAK descriptor, and Hamming distance matcher algorithms are implemented in a mobile AR application in order to evaluate the AR algorithm. The mobile AR application is run on the Samsung Note 9 smartphone. The image used in the evaluation is the Graffiti image from the Mikolajczyk data set. Graffiti image is fully printed in an A4 size paper and is attached to a wall with a height of 1.5m. This performance evaluation is able to evaluate the robustness of the AR algorithm in terms of brightness value from 0 Watts per square meter up to 70 Watts per square meter. The robustness of the AR algorithm in terms of scale invariance was evaluated from the distance of 5cm up to 50cm from the input image. The AR algorithm can obtain an accuracy of 83.49%, 70.89%, and 72.78% in terms of brightness changes, scale changes, and rotation changes respectively. This work introduces a more suitable performance evaluation for an AR application in a real controlled environment. [ABSTRACT FROM AUTHOR]

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IntroductionIn craniofacial surgery, magnetic navigation systems can effectively extend the doctor's limited visual range, improve their surgical precision, shorten the operation time, and reduce the incidence of surgical complications. Owing to the ease of magnetic navigation, the accuracy of the magnetic navigation system is affected by various equipment in the operating room. Therefore, its large-scale application is lacking because the navigation accuracy requirement can be extremely high during craniofacial surgery. Therefore, the accuracy of magnetic navigation systems is crucial. Various surgical instruments have been evaluated to effectively reduce the interference of magnetic navigation systems with surgical instruments. In craniofacial surgery, magnetic navigation systems can effectively extend the doctor's limited visual range, improve their surgical precision, shorten the operation time, and reduce the incidence of surgical complications. Owing to the ease of magnetic navigation, the accuracy of the magnetic navigation system is affected by various equipment in the operating room. Therefore, its large-scale application is lacking because the navigation accuracy requirement can be extremely high during craniofacial surgery. Therefore, the accuracy of magnetic navigation systems is crucial. Various surgical instruments have been evaluated to effectively reduce the interference of magnetic navigation systems with surgical instruments. In the surgical environment, the use of surgical instruments during mandibular surgery was simulated by selecting several conventional surgical instruments to record errors in the magnetic navigation system. The fluctuation values of the magnetic navigation errors were subsequently estimated and changes in its accuracy measured. MATLAB was used to calculate and analyze the fluctuations of the magnetic navigation errors. As results, the high-frequency electrosurgical system caused the greatest interference with the magnetic navigation system during surgery while powered on, with a maximum fluctuation error value of 1.8120 mm, and the maximum fluctuation error values of the stitch scissors, teeth forceps, and a needle holder were 1.3662, 1.3781, and 0.3912 mm, respectively. The closer the instrument is to the magnetic field generator or navigation target, the greater its impact. In conclusion, stitch scissors, teeth forceps, a needle holder, and the high-frequency electrosurgical system all affect magnetic navigation system accuracy. Therefore, it is necessary to avoid magnetic navigation system use and surgical instrument disturbances during surgery or select surgical instruments that do not interfere with the system. Surgical instruments must be evaluated for electromagnetic interference before they can be used in surgery with a magnetic navigation system. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

C-SAR/01 and C-SAR/02 serve as successors to the GF-3 satellite. They are designed to operate in tandem with GF-3, collectively forming a C-band synthetic aperture radar (SAR) satellite constellation. This constellation aims to achieve 1 m resolution imaging with a revisit rate of one day. It can effectively cater to various applications such as marine disaster prevention, monitoring marine dynamic environments, and supporting marine scientific research, disaster mitigation, environmental protection, and agriculture. Geometric correction plays a pivotal role in acquiring highly precise geographic location data for ground targets. The geometric positioning accuracy without control points signifies the SAR satellite's geometric performance. However, SAR images do not exhibit a straightforward image-point–object-point correspondence, unlike optical images. In this study, we introduce a novel approach employing high-precision automatic trihedral corner reflectors as ground control points (GCPs) to assess the geometric positioning accuracy of SAR images. A series of satellite-ground synchronization experiments was conducted at the Xilinhot SAR satellite calibration and validation site to evaluate the geometric positioning accuracy of different C-SAR image modes. Firstly, we calculated the azimuth and elevation angles of the corner reflectors based on satellite orbit parameters. During satellite transit, these corner reflectors were automatically adjusted to align with the radar-looking direction. We subsequently measured the exact longitude and latitude coordinates of the corner reflector vertex in situ using a high-precision real-time kinematics instrument. Next, we computed the theoretical image coordinates of the corner reflectors using the rational polynomial coefficients (RPC) model. After that, we determined the accurate position of the corner reflector in the Single Look Complex (SLC) SAR image using FFT interpolation and the sliding window method. Finally, we evaluated and validated the geometric positioning accuracy of C-SAR images by comparing the two coordinates. The preliminary results indicate that the positioning accuracy varies based on the satellite, imaging modes, and orbital directions. Nevertheless, for most sample points, the range positioning accuracy was better than 60 m, and the azimuth positioning accuracy was better than 80 m. These findings can serve as a valuable reference for subsequent applications of C-SAR satellites. [ABSTRACT FROM AUTHOR]

Accurate estimation of terrestrial evapotranspiration (ET) is crucial to understand the water cycle and partitioning of turbulent energy fluxes at the land surface. However, large-scale ET estimation is always difficult to achieve due to the complexity of controlling factors and the heterogeneity of landscapes. A recent model, referred to as the surface flux equilibrium (SFE) model, proposes that evaporative fraction (EF, the ratio of ET to available energy at the surface) can be estimated accurately from near-surface specific humidity and air temperature, which makes it applicable to common weather stations and climate reanalysis datasets. However, previous validation only focused on the evaluation of this model at site and basin scale for ET estimation, making the real performance of the SFE model for EF estimation heretofore unknown. There also exists a gap in the comparison between the SFE model and other existing products in the representation of global ET patterns and variation trend. Against this background, a comprehensive validation was performed in this study to evaluate the accuracy of the SFE model for both EF and ET estimation. The validation against 136 flux towers worldwide shows that the ground-based SFE model achieved EF estimation with a root-mean-square-error (RMSE) of 0.22 and 0.16 at daily and monthly scale, respectively. The corresponding RMSE of ET estimation was 0.94 mm/day and 16.37 mm/month. The application of the SFE model on a global scale was achieved by using two reanalysis products (ERA5-Land and GLDAS-CLSM) as inputs respectively. The results indicate that the SFE model did hold potential to reduce the RMSE of these two products for ET estimation, but the cost was the decrease in correlation coefficient (r). The comparison with five existing global ET products (BESS, GLEAM, AVHRR, MOD16, and SSEBop) shows that the SFE model performed on the basis of ERA5-Land product has achieved moderate accuracy among these products. Judged from r, mean absolute error, RMSE, and bias, its accuracy was ranked as fourth, fourth, second, and second, respectively. However, a further evaluation over different land cover types suggests that the ERA-based SFE model has significant superiority over cropland, relative superiority over forest and savannas, and significant inferiority over shrubland and grassland. This analysis is the first global validation of the SFE model for EF estimation as well as its comparison with existing global ET products. The validation analysis proves that this simple model has generally achieved comparable accuracy to existing complex models in the representation of global ET patterns and variation trend.

The international scientific community pays close attention to global land surface remote sensing mapping. Accurate LULC datasets are crucial for sustainable development assessments at various scales. Taking the southern China’s karst as an example, this study selected three sets CRLC-10m (CRLC Land Cover), ESRI-10m (ESRI Land Cover), and ESA-10m (ESA World Cover) were select for 2020 to conduct correlation analysis, spatial consistency analysis and accuracy evaluation. The results indicate that: (1) There is good correlation between ESRI-10m and ESA-10m; (2) Only 3.67% of the study area shows full consistency across all three datasets, with good spatial consistency in crop, forest, grassland types; (3) The overall accuracy of the land cover data sets in the study area is CRLC-10m (55.40%), ESRI-10m (55.26%), and ESA-10m (49.40%), and the accuracy of CRLC-10m is less affected by slope changes. This study provides theoretical guidance for selecting LULC data and enhancing remote sensing classification accuracy in karst regions.

With the recent increase in interest in machine learning and computer vision, camera-based pose estimation has emerged as a promising new technology. One of the most popular libraries for camera-based pose estimation is MediaPipe Pose due to its computational efficiency, ease of use, and the fact that it is open-source. However, little work has been performed to establish how accurate the library is and whether it is suitable for usage in, for example, physical therapy. This paper aims to provide an initial assessment of this. We find that the pose estimation is highly dependent on the camera’s viewing angle as well as the performed exercise. While high accuracy can be achieved under optimal conditions, the accuracy quickly decreases when the conditions are less favourable.

GF-14 satellite is a new generation of sub-meter stereo surveying and mapping satellite in China, carrying dual-line array stereo mapping cameras to achieve 1:10000 scale topographic mapping without Ground Control Points (GCPs). In fact, space-based high-precision mapping without GCPs is a challenging task that depends on the close cooperation of several payloads and links, of which on-orbit geometric calibration is one of the most critical links. In this paper, the on-orbit geometric calibration of the dual-line array cameras of GF-14 satellite was performed using the control points collected in the high-precision digital calibration field, and the calibration parameters of the dual-line array cameras were solved as a whole by alternate iterations of forward and backward intersection. On this basis, the location accuracy of the stereo images using the calibration parameters was preliminarily evaluated by using several test fields around the world. The evaluation result shows that the direct forward intersection accuracy of GF-14 satellite images without GCPs after on-orbit geometric calibration reaches 2.34 meters (RMS) in plane and 1.97 meters (RMS) in elevation.

To solve the complex weight matrix derivative problem when using the weighted least squares method to estimate the parameters of the mixed additive and multiplicative random error model (MAM error model), we use an improved artificial bee colony algorithm without derivative and the bootstrap method to estimate the parameters and evaluate the accuracy of MAM error model. The improved artificial bee colony algorithm can update individuals in multiple dimensions and improve the cooperation ability between individuals by constructing a new search equation based on the idea of quasi-affine transformation. The experimental results show that based on the weighted least squares criterion, the algorithm can get the results consistent with the weighted least squares method without multiple formula derivation. The parameter estimation and accuracy evaluation method based on the bootstrap method can get better parameter estimation and more reasonable accuracy information than existing methods, which provides a new idea for the theory of parameter estimation and accuracy evaluation of the MAM error model.

AbstractIntroductionIn craniofacial surgery, magnetic navigation systems can effectively extend the doctor’s limited visual range, improve their surgical precision, shorten the operation time, and reduce the incidence of surgical complications. Owing to the ease of magnetic navigation, the accuracy of the magnetic navigation system is affected by various equipment in the operating room. Therefore, its large-scale application is lacking because the navigation accuracy requirement can be extremely high during craniofacial surgery. Therefore, the accuracy of magnetic navigation systems is crucial. Various surgical instruments have been evaluated to effectively reduce the interference of magnetic navigation systems with surgical instruments. In craniofacial surgery, magnetic navigation systems can effectively extend the doctor’s limited visual range, improve their surgical precision, shorten the operation time, and reduce the incidence of surgical complications. Owing to the ease of magnetic navigation, the accuracy of the magnetic navigation system is affected by various equipment in the operating room. Therefore, its large-scale application is lacking because the navigation accuracy requirement can be extremely high during craniofacial surgery. Therefore, the accuracy of magnetic navigation systems is crucial. Various surgical instruments have been evaluated to effectively reduce the interference of magnetic navigation systems with surgical instruments. In the surgical environment, the use of surgical instruments during mandibular surgery was simulated by selecting several conventional surgical instruments to record errors in the magnetic navigation system. The fluctuation values of the magnetic navigation errors were subsequently estimated and changes in its accuracy measured. MATLAB was used to calculate and analyze the fluctuations of the magnetic navigation errors. As results, the high-frequency electrosurgical system caused the greatest interference with the magnetic navigation system during surgery while powered on, with a maximum fluctuation error value of 1.8120 mm, and the maximum fluctuation error values of the stitch scissors, teeth forceps, and a needle holder were 1.3662, 1.3781, and 0.3912 mm, respectively. The closer the instrument is to the magnetic field generator or navigation target, the greater its impact. In conclusion, stitch scissors, teeth forceps, a needle holder, and the high-frequency electrosurgical system all affect magnetic navigation system accuracy. Therefore, it is necessary to avoid magnetic navigation system use and surgical instrument disturbances during surgery or select surgical instruments that do not interfere with the system. Surgical instruments must be evaluated for electromagnetic interference before they can be used in surgery with a magnetic navigation system.

The virtual reference system--real-time kinematic (VRS--RTK) method can be used for the precise position monitoring of construction equipment, thus facilitating automation in the construction industry. However, the use of the VRS--RTK method is limited to construction sites where monitoring multiple construction equipment requires constant access to long-term evolution (LTE) data communication, which poses a cost issue and limits connections in accordance with the VRS--RTK service server capacity. To address these challenges, the broadcast real-time kinematic (B'RTK) method was developed. In this study, we aim to evaluate the B'RTK method as a new network RTK positioning service for construction automation in terms of RTK positioning performance and positioning accuracy in various global navigation satellite system (GNSS) reception environments by comparing it with various RTK methods. To assess B'RTK positioning accuracy, simultaneous observation was carried out using post-kinematic, single RTK, and VRS methods for each GNSS signal reception environment, and the accuracy of B'RTK positioning was evaluated by comparing the post-kinematic positioning results with other kinematic positioning results. The B'RTK positioning accuracy in actual construction sites is similar or superior to other kinematic positioning methods, including the VRS--RKT method. Therefore, the B'RTK method may be highly useful for the real-time monitoring of construction equipment and construction automation. [ABSTRACT FROM AUTHOR]

Land-use and land-cover changes in the Eastern European Plain have important implications for regional and global ecological environments, food security, and socio-economic development. Here, three 30 m resolution global land cover data products (FROM_GLC, GlobeLand30, and GLC_FCS30) from the Eastern European Plain were analyzed and evaluated for component similarity, type confusion degree, spatial consistency, and accuracy verification. The research found that the three products provided consistent descriptions of land-cover types in the East European Plain. There was a strong correlation in the type area between the different products, with a correlation coefficient >0.85. Medium-to-high-consistency areas represented 92.31% of the total plains area. The low-consistency areas were mainly concentrated on Yuzhny Island, Kola Peninsula, and Pechora River Basin. The comparison revealed high consistency among the three products in identifying forest, cropland, water, and permanent ice/snow types. However, the consistency was poor for shrubs, wetlands, and bare land. Using the GLCVSS_V1 validation dataset, the highest overall accuracy among the assessed land cover data products was observed in the FROM_GLC (73.96%), followed by GlobeLand30 (69.80%) and GLC_FCS30 (67.29%). The FROM_GLC dataset is suitable for studying forests, tundra, water, and providing an overall representation of the region's land cover. The GLC_FCS30 dataset is more suitable for agricultural research. The differences between products arise from the differences in classification systems, algorithms, and data correction. In the future, it will be necessary to utilize the advantages of different products for data fusion, focusing on areas with high heterogeneity and easily confused types, and improving the reliability of land-cover data products. [ABSTRACT FROM AUTHOR]

Accurate coastline position is of fundamental importance for many applications concerning coastal zone monitoring, management, and planning. For example, coastal erosion phenomena require a careful and continuous monitoring due to the dynamic nature of the coastline which can undergo sudden and significant changes in position and shape over time. Various techniques allow acquiring the coastline, among these the use of multispectral optical sensors operating from satellites is one of the most widespread. With the advent of high and very high geometric resolution (VHR) satellites, it is possible to obtain images with a pixel size of less than 1 m that allow extracting accurate coastlines. The purpose of this article is to define a fast approach to investigate the degree of accuracy of one of the most popular techniques for the automatic extraction of the coastline, based on the Normalized Difference Water Index (NDWI) use. In this study, the coastline is achieved from VHR Pléiades imagery (2 m for multispectral and 0.5 m for panchromatic). Therefore, NDWI is obtained and processed both from initial images and pan-sharpened images. The resulting coastlines are submitted to smoothing and their accuracy is therefore evaluated. For this purpose, a reference coastline is manually achieved from panchromatic image. Two different methods are proposed for coastline accuracy evaluation, both based on the geometrical analysis of the polygons generated by the intersection between the extracted coastline and the reference one. This study demonstrates that the proposed methods permit to easily evaluate the accuracy of the extracted coastline; in addition, the results confirm the effectiveness of NDWI and highlight the limited benefits of pan-sharpened images for this index application. [ABSTRACT FROM AUTHOR]

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Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The multi-channel temperature measurement system developed works with NTC thermistors. The article presents the results of theoretical and empirical evaluation of accuracy obtained in measurement channels. The basis for the theoretical assessment is the mathematical model for each of the measurement channels and the characteristics of the system elements included in the circuits of the measurement channel. Two different methods were used to theoretically estimate the accuracy: the Gauss Formula and the Monte Carlo method. The empirical assessment of accuracy is supported by measurement data collected during the calibration procedures for the device developed and the results of verification measurements performed on a working device. The exemplary results presented of the accuracy assessment, obtained in the selected channel, are representative of the properties of all other measurement channels. The results of both methods of assessing accuracy in measurement channels are very similar, which confirms the assumptions regarding the implementation of the instrument developed and its good metrological properties. [ABSTRACT FROM AUTHOR]

Laparoscopy is employed in conventional minimally invasive surgery to inspect internal cavities by viewing two-dimensional images on a monitor. This method has a limited field of view and provides insufficient information for surgeons, increasing surgical complexity. Utilizing simultaneous localization and mapping (SLAM) technology to reconstruct laparoscopic scenes can offer more comprehensive and intuitive visual feedback. Moreover, the precision of the reconstructed models is a crucial factor for further applications of surgical assistance systems. However, challenges such as data scarcity and scale uncertainty hinder effective assessment of the accuracy of endoscopic monocular SLAM reconstructions. Therefore, this paper proposes a technique that incorporates existing knowledge from calibration objects to supplement metric information and resolve scale ambiguity issues, and it quantifies the endoscopic reconstruction accuracy based on local alignment metrics. The experimental results demonstrate that the reconstructed models restore realistic scales and enable error analysis for laparoscopic SLAM reconstruction systems. This suggests that for the evaluation of monocular SLAM three-dimensional (3D) reconstruction accuracy in minimally invasive surgery scenarios, our proposed scheme for recovering scale factors is viable, and our evaluation outcomes can serve as criteria for measuring reconstruction precision. [ABSTRACT FROM AUTHOR]

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Copyright of China Mechanical Engineering is the property of Editorial Board of China Mechanical Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As a major component of water cycle, the accuracy quantification of different precipitation products is critical for evaluating climate change and ecosystem functions. However, a lack of evidence is available to choose a precise precipitation product in relative applications. Here, to solve this limit, we analyze the spatiotemporal pattern and accuracy of four precipitation products, including CHIRPS V2.0, PERSIANN-CDR, ECMWF ERA5-Land, and GLDAS_NOAH025_3H, over China during the period of 1981–2020, based on the five precipitation traits (i.e., spatial pattern of multi-year average, annual trend, seasonality, frequency, and intensity), and meteorological gauge observations are taken as the benchmark. Our results show that, compared to other products, CHIRPS data has the strongest ability to present spatial pattern of multi-year average precipitation, especially in most parts of northeastern and southern China, and ERA5 has the weakest ability to simulate the multi-year average precipitation. All four precipitation products can accurately depict the spatial pattern of seasonality, among which CHIRPS and ERA5 have the highest and lowest fitting ability, respectively, but four products poorly describe the spatial pattern of precipitation intensity and frequency at a daily scale. These products only correctly predict the interannual precipitation trend in some local areas. Our findings provide evidences to select high-quality precipitation data, and could help to improve the accuracy of relative geophysical models. [ABSTRACT FROM AUTHOR]

Quantifying the accuracy of burned area (BA) estimations is crucial in wildfire monitoring and loss assessment based on remote sensing technology. In this letter, a novel approach to quantitatively evaluate the accuracy of BA estimations, called the vector distance algorithm (VDA), was proposed based on boundary sampling and $t$ tests. To validate the effectiveness of this approach, Sentinel-2 images were used to estimate the BA, and a field survey and a GaoFen-6 (GF-6) image were then utilized to evaluate the accuracy based on the VDA. The results were as follows: 1) the proposed algorithm could provide not only the percent accuracy of the evaluation but also the confidence interval of the BA; 2) the accuracy validation of the BA extracted by the normalized burn ratio (NBR) index and normalized difference vegetation index (NDVI) was verified by the VDA; 3) based on the field survey, the VDA confirmed that the NBR index had high accuracy, while the NDVI index had a large error, which is consistent with the results of using ground truth observations as a reference; and 4) the analysis based on the GF-6 image showed similar results. This study indicated that the VDA is effective and has the potential for widespread use in evaluating the accuracy of a BA. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Geodesy & Geodynamics (1671-5942) is the property of Editorial Board Journal of Geodesy & Geodynamics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)